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A Pairwise Deep Ranking Model for Relative Assessment of Parkinson's Disease Patients from Gait Signals

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dc.contributor.author Oǧul, Burçin Buket
dc.contributor.author Özdemir, Suat
dc.date.accessioned 2024-02-14T07:49:23Z
dc.date.available 2024-02-14T07:49:23Z
dc.date.issued 2022
dc.identifier.citation Oǧul, Burçin Buket; Özdemir, S. (2022). "A Pairwise Deep Ranking Model for Relative Assessment of Parkinson's Disease Patients from Gait Signals", IEEE Access, Vol.10, pp.6676-6683. tr_TR
dc.identifier.issn 21693536
dc.identifier.uri http://hdl.handle.net/20.500.12416/7197
dc.description.abstract Continuous monitoring of the symptoms is crucial to improve the quality of life for patients with Parkinson's Disease (PD). Thus, it is necessary to objectively assess the PD symptoms. Since manual assessment is subjective and prone to misinterpretation, computer-aided methods that use sensory measurements have recently been used to make objective PD assessment. Current methods follow an absolute assessment strategy, where the symptoms are classified into known categories or quantified with exact values. These methods are usually difficult to generalize and considered to be unreliable in practice. In this paper, we formulate the PD assessment problem as a relative assessment of one patient compared to another. For this assessment, we propose a new approach to the comparative analysis of gait signals obtained via foot-worn sensors. We introduce a novel pairwise deep-ranking model that is fed by data from a pair of patients, where the data is obtained from multiple ground reaction force sensors. The proposed model, called Ranking by Siamese Recurrent Network with Attention, takes two multivariate time-series as inputs and produces a probability of the first signal having a higher continuous attribute than the second one. In ten-fold cross-validation, the accuracy of pairwise ranking predictions can reach up to 82% with an AUROC of 0.89. The model outperforms the previous methods for PD monitoring when run in the same experimental setup. To the best of our knowledge, this is the first study that attempts to relatively assess PD patients using a pairwise ranking measure on sensory data. The model can serve as a complementary model to computer-aided prognosis tools by monitoring the progress of the patient during the applied treatment. tr_TR
dc.language.iso eng tr_TR
dc.relation.isversionof 10.1109/ACCESS.2021.3136724 tr_TR
dc.rights info:eu-repo/semantics/openAccess tr_TR
dc.subject Gait Analysis tr_TR
dc.subject Long Short-Term Memory tr_TR
dc.subject Pairwise Ranking tr_TR
dc.subject Parkinson's Disease tr_TR
dc.subject Siamese Network tr_TR
dc.title A Pairwise Deep Ranking Model for Relative Assessment of Parkinson's Disease Patients from Gait Signals tr_TR
dc.type article tr_TR
dc.relation.journal IEEE Access tr_TR
dc.identifier.volume 10 tr_TR
dc.identifier.startpage 6676 tr_TR
dc.identifier.endpage 6683 tr_TR
dc.contributor.department Çankaya Üniversitesi, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü tr_TR


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